Global Challenges, Local Solutions: Transforming Agri-Food Systems from Production to Consumption

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Economics, Policies and Rural Management".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 2307

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Department of Geography, University of Barcelona, 08001 Barcelona, Spain
Interests: rural geography; regional geography; post-productivism agriculture; rural tourism; gastronomy; landscape’s perception
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Special Issue Information

Dear Colleagues,

Traditionally, agricultural spaces have been interpreted in economic terms as production spaces in which the agri-food component prevailed. In recent decades, the countryside, first in the most technologically advanced countries and then in the rest, has been transformed, ceasing to be a space specialized, almost entirely, in food production to become a multifunctional space where consumption activities are increasingly important. At the same time, cities, unlinked from agricultural activity in the last hundred years, seem to be rediscovering part of their links with the agrarian world that has always sustained them. These circumstances are conditioned by global change that affects all areas and actors involved in agri-food systems. It is in this context that the recent trends that advocate the creation, reinforcement, or rediscovery of local food must be framed.

This Special Issue seeks contributions that show the latest advances in this field from a multidisciplinary perspective (from Geography, Economics, Sociology, and Social Sciences in general). Contributions that have a transdisciplinary approach and those that generate spaces for dialog between the natural sciences and the human sciences will also be welcome. Some of the topics that could be included are as follows: challenges and uncertainties of primary production of local food, transport, and last mile issues; role of sellers’ and merchants’ attitudes in local food promotion; contradictions in consumption; conceptualization of local food; local food governances; ecological footprint of local foods; relationships between local agriculture and the city; local husbandry and consumption; local foodscapes; local food and tourism; case studies; etc.

Dr. Xosé Antón Armesto-López
Guest Editor

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Keywords

  • new local agri-food models
  • proximity food
  • ecological footprint
  • ethical consumption
  • urban and periurban agriculture
  • marginal agricultural areas
  • local food ethical issues

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Published Papers (2 papers)

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Research

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16 pages, 914 KB  
Article
Research on Heterogeneous Behavior in Green Food Trademark Applications: Evidence from Farmers’ Professional Cooperatives and Agricultural Enterprises in Heilongjiang Province, China
by Cheng Guo, Hong Chen and Hongpeng Guo
Agriculture 2026, 16(4), 466; https://doi.org/10.3390/agriculture16040466 - 18 Feb 2026
Cited by 1 | Viewed by 631
Abstract
As a certification mark for agricultural product quality, the Green Food Trademark plays a pivotal role in ensuring food safety, promoting sustainable agricultural development, and enhancing market competitiveness. In China, farmers’ professional cooperatives and agricultural enterprises constitute the primary applicants for the Green [...] Read more.
As a certification mark for agricultural product quality, the Green Food Trademark plays a pivotal role in ensuring food safety, promoting sustainable agricultural development, and enhancing market competitiveness. In China, farmers’ professional cooperatives and agricultural enterprises constitute the primary applicants for the Green Food Trademark. Differences in ownership structure, operational objectives, and policy incentives directly influence the behavioral intentions of these applicants. This study employs the Theory of Planned Behavior and SHAP explainable machine learning to construct an evaluation framework for Green Food Trademark application behavior among operators. Utilizing a random forest model and based on 312 questionnaires from a stratified sample of farmers’ professional cooperatives and enterprises in Heilongjiang Province, it examines behavioral factors and their variations during the Green Food Trademark application process. Findings reveal procedural barriers as the primary inhibitor and utilitarian incentives as the main driver. Significant differences exist in both the direction and degree of influence exerted by identical factors on the Green Food Trademark application behavior of farmers’ professional cooperatives and agricultural enterprises. It is recommended that differentiated policies be formulated to enhance the Green Food Trademark application behavior intentions of farmers’ professional cooperatives and agricultural enterprises, thereby driving the promotion and realization of green standardized agricultural production. Full article
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Review

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22 pages, 1211 KB  
Review
A Bibliometric Review of Machine Learning for Sustainable Agri-Food Systems: Evolution, Collaboration Networks, and Future Directions
by Segundo Jonathan Rojas-Flores, Rafael Liza, Renny Nazario-Naveda, Félix Díaz, Daniel Delfin-Narciso and Moisés Gallozzo Cardenas
Agriculture 2026, 16(4), 462; https://doi.org/10.3390/agriculture16040462 - 16 Feb 2026
Cited by 3 | Viewed by 1127
Abstract
Global agri-food systems face a critical conflict between the need to feed a growing population and the imperative to mitigate its substantial environmental impact, including 23% of global greenhouse gas emissions and 70% of freshwater withdrawals. This bibliometric review maps the scientific landscape [...] Read more.
Global agri-food systems face a critical conflict between the need to feed a growing population and the imperative to mitigate its substantial environmental impact, including 23% of global greenhouse gas emissions and 70% of freshwater withdrawals. This bibliometric review maps the scientific landscape of Machine Learning (ML) research applied to sustainable agri-food systems. Using a structured bibliometric protocol, we analyzed 648 scientific documents from Scopus (2010–2025) to map the evolution, collaborative networks, and thematic trends in this domain. Results reveal a field that has grown exponentially until 2021, primarily driven by contributions from Computer Science (26%) and Engineering (21%), with key publications in journals such as Computers and Electronics in Agriculture (22 papers, 2631 citations). While China and India lead in productivity (80% of top authors), high-impact research remains strongly linked to international collaborations with institutions in the U.S. and EU. Current ML efforts focus on technical optimization—such as precision irrigation, pest detection, and yield prediction—but fall short in addressing social equity and climate resilience. The study concludes that while ML holds significant promise for sustainable agri-food processing and system optimization, future progress depends on overcoming fragmented regional collaborations and integrating holistic frameworks, such as life-cycle assessment, to ensure resilient and equitable food systems. Full article
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